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http://hdl.handle.net/2080/73

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DC Field

Value

Language

dc.contributor.author

Dash, P K

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dc.contributor.author

Saha, S

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dc.contributor.author

Nanda, P K

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dc.date.accessioned

2005-06-28T09:38:47Z

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dc.date.available

2005-06-28T09:38:47Z

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dc.date.issued

1991

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dc.identifier.citation

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, 23-26 July 1991, Seattle, WA, P 247-250

en

dc.identifier.uri

http://hdl.handle.net/2080/73

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dc.description

Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

en

dc.description.abstract

The authors propose a new methodology for controlling multitap capacitors in a power system using a three layer feedforward neural network. The neural network, in the proposed scheme is separately trained with two algorithms namely backpropagation and a combined backpropagation-Cauchy's learning algorithm. Studies on 30 bus IEEE test system are carried out and quite satisfactory results are obtained. The inputs to the net are the real power, reactive power and voltage magnitude at a few selected buses and the network's outputs are the values of capacitive Var injection. Performance comparison is made between two algorithms and the combined backpropagation-Cauchy's algorithm is found to be better than the other

en

dc.format.extent

279945 bytes

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dc.format.mimetype

application/pdf

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dc.language.iso

en

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dc.publisher

IEEE

en

dc.subject

backpropagation

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dc.subject

feedforward neural nets

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dc.subject

power capacitors

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dc.subject

reactive power

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dc.title

Artificial neural net approach for capacitor placement in power system